The development and psychometric properties of an educational development impact questionnaire
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The purpose of this study was to develop and provide psychometric evidence of the Educational Development Impact Questionnaire (EDIQ) for evaluating complex outcomes of multifaceted educational development work within centres of teaching and learning. Our study addresses the need for a user-friendly and psychometrically sound survey instrument adaptable to differing higher education contexts. Our outcomes framework, mapping the intended short- and medium-term outcomes of our centre’s educational development activities, provided the necessary framework on which to create survey items. Scores from 267 instructors from a research-intensive university provided the data for examining the EDIQ’s initial factor structure using exploratory factor analysis. We justify our use of two different cut-scores to generate two models of factor structures. Factors common to both models are instructor growth, scholarship of teaching and learning, technology integration, impact on students, and knowledge enhancement. We discuss how the models provide different opportunities for assessing short-term and medium-term outcomes over time and the usefulness of the EDIQ beyond the current study context.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it